Best AI tools for< Accelerate Ml >
20 - AI tool Sites
Striveworks
Striveworks is an AI application that offers a Machine Learning Operations Platform designed to help organizations build, deploy, maintain, monitor, and audit machine learning models efficiently. It provides features such as rapid model deployment, data and model auditability, low-code interface, flexible deployment options, and operationalizing AI data science with real returns. Striveworks aims to accelerate the ML lifecycle, save time and money in model creation, and enable non-experts to leverage AI for data-driven decisions.
Hugging Face
Hugging Face is an AI community platform that facilitates collaboration on models, datasets, and applications. It offers a wide range of tools and resources for machine learning enthusiasts to create, discover, and share their work. With a focus on advancing the field of artificial intelligence, Hugging Face provides a space for developers, researchers, and organizations to accelerate their ML projects.
SiMa.ai
SiMa.ai is an AI application that offers high-performance, power-efficient, and scalable edge machine learning solutions for various industries such as automotive, industrial, healthcare, drones, and government sectors. The platform provides MLSoC™ boards, DevKit 2.0, Palette Software 1.2, and Edgematic™ for developers to accelerate complete applications and deploy AI-enabled solutions. SiMa.ai's Machine Learning System on Chip (MLSoC) enables full-pipeline implementations of real-world ML solutions, making it a trusted platform for edge AI development.
Rafay
Rafay is an AI-powered platform that accelerates cloud-native and AI/ML initiatives for enterprises. It provides automation for Kubernetes clusters, cloud cost optimization, and AI workbenches as a service. Rafay enables platform teams to focus on innovation by automating self-service cloud infrastructure workflows.
syntheticAIdata
syntheticAIdata is a platform that provides synthetic data for training vision AI models. Synthetic data is generated artificially, and it can be used to augment existing real-world datasets or to create new datasets from scratch. syntheticAIdata's platform is easy to use, and it can be integrated with leading cloud platforms. The company's mission is to make synthetic data accessible to everyone, and to help businesses overcome the challenges of acquiring high-quality data for training their vision AI models.
UBIAI
UBIAI is a powerful text annotation tool that helps businesses accelerate their data labeling process. With UBIAI, businesses can annotate any type of document, including PDFs, images, and text. UBIAI also offers a variety of features to make the annotation process easier and more efficient, such as auto-labeling, multi-lingual annotation, and team collaboration. With UBIAI, businesses can save time and money on their data labeling projects.
Pandio
Pandio is an AI orchestration platform that simplifies data pipelines to harness the power of AI. It offers cloud-native managed solutions to connect systems, automate data movement, and accelerate machine learning model deployment. Pandio's AI-driven architecture orchestrates models, data, and ML tools to drive AI automation and data-driven decisions faster. The platform is designed for price-performance, offering data movement at high speed and low cost, with near-infinite scalability and compatibility with any data, tools, or cloud environment.
Tricentis
Tricentis is an AI-powered testing tool that offers a comprehensive set of test automation capabilities to address various testing challenges. It provides end-to-end test automation solutions for a wide range of applications, including Salesforce, mobile testing, performance testing, and data integrity testing. Tricentis leverages advanced ML technologies to enable faster and smarter testing, ensuring quality at speed with reduced risk, time, and costs. The platform also offers continuous performance testing, change and data intelligence, and model-based, codeless test automation for mobile applications.
Alluxio
Alluxio is a data orchestration platform designed for the cloud, offering seamless access, management, and running of AI/ML workloads. Positioned between compute and storage, Alluxio provides a unified solution for enterprises to handle data and AI tasks across diverse infrastructure environments. The platform accelerates model training and serving, maximizes infrastructure ROI, and ensures seamless data access. Alluxio addresses challenges such as data silos, low performance, data engineering complexity, and high costs associated with managing different tech stacks and storage systems.
Insitro
Insitro is a drug discovery and development company that uses machine learning and data to identify and develop new medicines. The company's platform integrates in vitro cellular data produced in its labs with human clinical data to help redefine disease. Insitro's pipeline includes wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience.
USM Business Systems
USM Business Systems is a leading AI mobile app development company in the USA and Europe. They offer a wide range of services including workforce management, data quality solutions, cloud migration, HR management, and mobile app development. With a focus on artificial intelligence and machine learning, they help businesses accelerate their digital transformation and boost productivity. USM provides custom AI app development services tailored to each client's unique needs, delivering innovative solutions that enhance market value. They also offer workforce services, AI engineering, and top-notch staff augmentation services. USM is committed to providing quality customer service and helping clients unlock new opportunities through advanced AI technology.
Seldon
Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.
Voxel51
Voxel51 is an AI tool that provides open-source computer vision tools for machine learning. It offers solutions for various industries such as agriculture, aviation, driving, healthcare, manufacturing, retail, robotics, and security. Voxel51's main product, FiftyOne, helps users explore, visualize, and curate visual data to improve model performance and accelerate the development of visual AI applications. The platform is trusted by thousands of users and companies, offering both open-source and enterprise-ready solutions to manage and refine data and models for visual AI.
xZactly.ai
xZactly.ai is an AI tool that works with Artificial Intelligence start-up businesses and high growth companies to deliver accelerated revenue growth, AI specific sales, business development, and marketing expertise, seed and venture capital financing, business scale, and global expansion fund. The tool helps in connecting businesses and investors, providing go-to-market strategies, and offering triage, transformation, and turnaround solutions for AI and ML companies. With over 25 years of experience in sales, marketing, and business development, xZactly.ai aims to accelerate sales and boost revenue for AI-driven businesses by delivering expertise, strategy, and execution.
Toloka AI
Toloka AI is a data labeling platform that empowers AI development by combining human insight with machine learning models. It offers adaptive AutoML, human-in-the-loop workflows, large language models, and automated data labeling. The platform supports various AI solutions with human input, such as e-commerce services, content moderation, computer vision, and NLP. Toloka AI aims to accelerate machine learning processes by providing high-quality human-labeled data and leveraging the power of the crowd.
ClosedLoop
ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs by providing accurate, explainable, and actionable predictions of individual-level health risks. The platform offers predictive analytics for various healthcare sectors, data science automation, and a healthcare content library to accelerate time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations, enabling proactive interventions, improved clinical outcomes, and innovative healthcare offerings.
BugRaptors
BugRaptors is an AI-powered quality engineering services company that offers a wide range of software testing services. They provide manual testing, compatibility testing, functional testing, UAT services, mobile app testing, web testing, game testing, regression testing, usability testing, crowd-source testing, automation testing, and more. BugRaptors leverages AI and automation to deliver world-class QA services, ensuring seamless customer experience and aligning with DevOps automation goals. They have developed proprietary tools like MoboRaptors, BugBot, RaptorVista, RaptorGen, RaptorHub, RaptorAssist, RaptorSelect, and RaptorVision to enhance their services and provide quality engineering solutions.
Baseten
Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.
Union.ai
Union.ai is an infrastructure platform designed for AI, ML, and data workloads. It offers a scalable MLOps platform that optimizes resources, reduces costs, and fosters collaboration among team members. Union.ai provides features such as declarative infrastructure, data lineage tracking, accelerated datasets, and more to streamline AI orchestration on Kubernetes. It aims to simplify the management of AI, ML, and data workflows in production environments by addressing complexities and offering cost-effective strategies.
Dflux
Dflux is a cloud-based Unified Data Science Platform that offers end-to-end data engineering and intelligence with a no-code ML approach. It enables users to integrate data, perform data engineering, create customized models, analyze interactive dashboards, and make data-driven decisions for customer retention and business growth. Dflux bridges the gap between data strategy and data science, providing powerful SQL editor, intuitive dashboards, AI-powered text to SQL query builder, and AutoML capabilities. It accelerates insights with data science, enhances operational agility, and ensures a well-defined, automated data science life cycle. The platform caters to Data Engineers, Data Scientists, Data Analysts, and Decision Makers, offering all-round data preparation, AutoML models, and built-in data visualizations. Dflux is a secure, reliable, and comprehensive data platform that automates analytics, machine learning, and data processes, making data to insights easy and accessible for enterprises.
20 - Open Source AI Tools
fuse-med-ml
FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.
awesome-mlops
Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.
ml-road-map
The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.
neptune-client
Neptune is a scalable experiment tracker for teams training foundation models. Log millions of runs, effortlessly monitor and visualize model training, and deploy on your infrastructure. Track 100% of metadata to accelerate AI breakthroughs. Log and display any framework and metadata type from any ML pipeline. Organize experiments with nested structures and custom dashboards. Compare results, visualize training, and optimize models quicker. Version models, review stages, and access production-ready models. Share results, manage users, and projects. Integrate with 25+ frameworks. Trusted by great companies to improve workflow.
Awesome-LLM-Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
intel-extension-for-transformers
Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).
awesome-ai-tools-for-game-dev
This repository is a curated collection of powerful AI tools that accelerate and enhance game development. It provides tools for asset, texture, image, code generation, animation video mocap, voice generation, speech recognition, conversational models, game design, search engine, AI NPC, Python libraries, and C# libraries. These tools streamline the creation process, save time, automate tasks, and unlock creative possibilities for game developers, whether indie or part of a studio. The repository aims to speed up development and enable the creation of immersive games by leveraging cutting-edge AI technologies.
jina
Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.
ivy
Ivy is an open-source machine learning framework that enables users to convert code between different ML frameworks and write framework-agnostic code. It allows users to transpile code from one framework to another, making it easy to use building blocks from different frameworks in a single project. Ivy also serves as a flexible framework that breaks free from framework limitations, allowing users to publish code that is interoperable with various frameworks and future frameworks. Users can define trainable modules and layers using Ivy's stateful API, making it easy to build and train models across different backends.
awesome-RLAIF
Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.
MegaDetector
MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".
SwanLab
SwanLab is an open-source, lightweight AI experiment tracking tool that provides a platform for tracking, comparing, and collaborating on experiments, aiming to accelerate the research and development efficiency of AI teams by 100 times. It offers a friendly API and a beautiful interface, combining hyperparameter tracking, metric recording, online collaboration, experiment link sharing, real-time message notifications, and more. With SwanLab, researchers can document their training experiences, seamlessly communicate and collaborate with collaborators, and machine learning engineers can develop models for production faster.
swift
SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.
LLaMA-Factory
LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.
ivy
Ivy is an open-source machine learning framework that enables you to: * 🔄 **Convert code into any framework** : Use and build on top of any model, library, or device by converting any code from one framework to another using `ivy.transpile`. * ⚒️ **Write framework-agnostic code** : Write your code once in `ivy` and then choose the most appropriate ML framework as the backend to leverage all the benefits and tools. Join our growing community 🌍 to connect with people using Ivy. **Let's** unify.ai **together 🦾**
LLMSys-PaperList
This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.
Mastering-GitHub-Copilot-for-Paired-Programming
Mastering GitHub Copilot for AI Paired Programming is a comprehensive course designed to equip you with the skills and knowledge necessary to harness the power of GitHub Copilot, an AI-driven coding assistant. Through a series of engaging lessons, you will learn how to seamlessly integrate GitHub Copilot into your workflow, leveraging its autocompletion, customizable features, and advanced programming techniques. This course is tailored to provide you with a deep understanding of AI-driven algorithms and best practices, enabling you to enhance code quality and accelerate your coding skills. By embracing the transformative power of AI paired programming, you will gain the tools and confidence needed to succeed in today's dynamic software development landscape.
open-ai
Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.
7 - OpenAI Gpts
Material Tailwind GPT
Accelerate web app development with Material Tailwind GPT's components - 10x faster.
Tourist Language Accelerator
Accelerates the learning of key phrases and cultural norms for travelers in various languages.
Digital Entrepreneurship Accelerator Coach
The Go-To Coach for Aspiring Digital Entrepreneurs, Innovators, & Startups. Learn More at UnderdogInnovationInc.com.
24 Hour Startup Accelerator
Niche-focused startup guide, humorous, strategic, simplifying ideas.
Backloger.ai - Product MVP Accelerator
Drop in any requirements or any text ; I'll help you create an MVP with insights.
Digital Boost Lab
A guide for developing university-focused digital startup accelerator programs.